Description Usage Arguments Value Author(s) References See Also Examples
Calculates the acoustic saturation index (saturation per minute/row).
1 2 3 |
pow |
NxN matrix of Power (POW) values (must match dimensions of bgn) |
bgn |
NxN matrix of Background Noise (BGN) values (must match dimensions of pow) |
min.db |
Minimum decibels (default is 0) |
max.db |
Maximum decibels (default is 6.5) |
p |
Threshold percentile for bgn, default is p=0.90 (90th percentile) |
noise |
Remove noise in the BGN. Default is null and valid options are mean or median) |
decibels |
(FALSE/TRUE) If TRUE, transform the data, assuming Hz, to decibels using Db=20*log(x) |
raw.freq |
(FALSE/TRUE) Return raw frequencies rather than normalized |
probs |
(FALSE/TRUE) Return a vector of row-wise probabilities indicating saturation |
smooth |
(FALSE/TRUE) Smooth the raw or normalized frequencies |
window |
Size of window to use in smoothing |
A vector equal to nrow(pow & bgn)
Jeffrey S. Evans <jeffrey_evans@tnc.org> and Tim Boucher <tboucher@tnc.org>
Burivalova, Z., M. Towsey, T. Boucher, A. Truskinger, P. Roe & E.T. Game (2018) Using soundscapes to detect variable degrees of human influence on tropical forests in Papua New Guinea. Conservation Biology 329(1):205-215
sma
for details on ARIMA smoothing
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | # Small example
POW <- matrix(runif(2000, 0, 15), nrow = 100, ncol = 20)
BGN <- matrix(runif(2000, 0, 15), nrow = 100, ncol = 20)
# Normalized (raw.freq = FALSE) and smoothed (smooth=TRUE)
summary( sat <- acoustic.saturation(POW, BGN, smooth = TRUE) )
# Return probability of saturation
( sat <- acoustic.saturation(POW, BGN, probs = TRUE) )
# Large example
rtnorm <- function(n, mean, sd, a = -Inf, b = Inf){
qnorm(runif(n, pnorm(a, mean, sd), pnorm(b, mean, sd)), mean, sd)
}
POW <- matrix(rtnorm(368640, mean = 1.60741, sd = 1.66311, a = 0, b = 23.4575),
nrow = 1440, ncol = 256)
BGN <- matrix(rtnorm(368640, mean = 1.60741, sd = 1.66311, a = 0, b = 23.4575),
nrow = 1440, ncol = 256)
# Normalized (raw.freq = FALSE) and smoothed (smooth=TRUE)
summary( sat <- acoustic.saturation(POW, BGN, smooth = TRUE) )
# Not normalized (raw.freq = TRUE) and not smoothed (smooth=FALSE)
summary( sat <- acoustic.saturation(POW, BGN, raw.freq = TRUE, smooth = FALSE) )
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